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location_risk_report

Read-onlyIdempotent

One-call, site-bound hazard + environmental profile with an explainable 0-100 risk score. Geocodes an address (or takes lat/lon) then fans out to FEMA flood zone, recent FEMA disaster declarations (county), active NWS alerts, USGS earthquakes within 50km, EPA ECHO regulated facilities (by ZIP/state), and parcel records (Maryland statewide / Texas-Harris County only). The score starts at 100 and subtracts itemized deductions (flood SFHA, active alerts, declarations, EPA non-compliance, strong quakes). A failing source is noted, not fatal. Cross-source synthesis; not a substitute for a professional site assessment.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latNoLatitude (use with lon instead of address).
lonNoLongitude (use with lat instead of address).
stateNoOptional 2-letter state override (helps EPA/parcel/declaration scoping).
addressNoFull US street address to geocode.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.2/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond the readOnly/idempotent/non-destructive annotations, the description discloses meaningful behavioral traits: the scoring mechanism (starts at 100 and subtracts deductions), partial geographic coverage for parcel records (Maryland/Harris County only), and failure tolerance ('A failing source is noted, not fatal'). This is rich, useful context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is information-dense but well-organized: headline output, data sources, scoring logic, failure behavior, and caveat. Every clause earns its place, though it is slightly long and could be parsed more easily with sentence breaks or lists.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a complex multi-source tool with no output schema, the description is complete: it covers input alternatives (address vs lat/lon), source coverage, geospatial scope, scoring, partial-failure behavior, and the professional-assessment disclaimer. An agent has enough to invoke it correctly and set expectations.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The description adds some context by noting geocoding accepts an address or lat/lon, and that state can override EPA/parcel/declaration scoping, but this mostly mirrors the schema descriptions rather than adding substantial new meaning.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool produces a composite site-bound hazard and environmental profile with an explainable 0-100 risk score, and enumerates the exact data sources it fans out to. This distinguishes it from single-source siblings like flood_zone_lookup or earthquake_recent and similar aggregate tools by emphasizing 'one-call' cross-source synthesis.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The usage context is implied: use this when you need a consolidated multi-source location risk profile, since it 'fans out' to many datasets. However, it never explicitly says when to choose this tool over environmental_site_risk or lane_location_risk_pack, nor does it state any exclusions or prerequisites.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

B3.3/5.0
Disambiguation2/5

Several tool clusters overlap heavily—company due-diligence and risk tools (counterparty_risk_score, company_trust_check, entity_dossier, issuer_diligence_dossier, resolve_entity, entity_resolve), carrier vetting tools, sanctions screening tools, and recall tools all have subtle boundary distinctions. While descriptions are detailed, an agent navigating 294 tools will frequently struggle to pick the right one.

Naming Consistency3/5

Most tools follow a readable snake_case domain-prefix pattern (fdic_, edgar_, sanctions_, congress_), which helps. However, verb placement is inconsistent—search_available_datasets vs cdc_dataset_query, resolve_entity vs entity_resolve—and synonyms like search, lookup, get, detail, fetch, and status are used interchangeably.

Tool Count1/5

294 tools is an extreme number for a single MCP server, far beyond what an agent can reliably hold in context or select from accurately. The presence of tool-group discovery helpers mitigates but does not solve the fundamental scale problem.

Completeness4/5

The data breadth is genuinely extensive, covering finance, health, legal, real estate, transportation, energy, cyber, education, and many other domains, often with generic query fallbacks. Still, some capabilities are shallow or incomplete—package tracking stops at a link, property tools are demo-only in places, and caselaw coverage is limited—so it is not a fully complete surface.